Node Importance Algorithm for Directed Weighted Networks Based on Degree Difference and Standardized K-order Propagation Number

Tao Tang

doi:10.47297/taposatWSP2633-456903.20250611

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Node Importance Algorithm for Directed Weighted Networks Based on Degree Difference and Standardized K-order Propagation Number

Volume 6, Issue 11

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Node Importance Algorithm for Directed Weighted Networks Based on Degree Difference and Standardized K-order Propagation Number
Node Importance Algorithm for Directed Weighted Networks Based on Degree Difference and Standardized K-order Propagation Number
Hunan University of Science and Technology, Xiangtan, Hunan, 411201, China
Vol. 6, Issue 11, Pages: 15-20(2025)
Published：30 November 2025
DOI：10.47297/taposatWSP2633-456903.20250611
Accepted：

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Tao Tang.Node Importance Algorithm for Directed Weighted Networks Based on Degree Difference and Standardized K-order Propagation Number[J].Theory and Practice of Science and Technology,2025,06(11):15-20.
DOI：

Tao Tang.Node Importance Algorithm for Directed Weighted Networks Based on Degree Difference and Standardized K-order Propagation Number[J].Theory and Practice of Science and Technology,2025,06(11):15-20. DOI： 10.47297/taposatWSP2633-456903.20250611.

摘要

Abstract

To overcome the shortcomings of existing key node identification techniques

such as high computational complexity

single evaluation dimension

and limited application scope

a new algorithm for key node evaluation is constructed. This algorithm addresses the need for synergistic evaluation of "node's own role characteristics" and "multi-order information propagation capability" in directed weighted networks. First

the degree difference is used to characterize the disparity between in-degree and out-degree

combined with cross strength to reflect the node's own connection characteristics

thereby quantifying the node's own role and solving the problem that "weight/number of connections alone cannot distinguish in-degree and out-degree differences". Second

the algorithm applies Min-Max normalization to weights to optimize path length

and comprehensively measures the multi-step propagation influence of nodes through multi-order K-order propagation number and structural entropy calculation. The final results show that the algorithm achieves certain improvements in accuracy

effectiveness

and identification capability.

关键词

Keywords

references

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Lan, C. J., Sun, W. J., Yu, L. F., et al. (2025). Identification of important nodes in directed weighted networks based on degree difference and effective neighbor distance. Complex Systems and Complexity Science, n. vol.(n. iss.), 1–9.

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